Triple
T20614957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Paulo to Frankfurt |
E506541
|
entity |
| Predicate | servedCityRoleTo |
P8234
|
FINISHED |
| Object | major German aviation hub |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: major German aviation hub | Statement: [São Paulo to Frankfurt, servedCityRoleTo, major German aviation hub]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedCityRoleTo Context triple: [São Paulo to Frankfurt, servedCityRoleTo, major German aviation hub]
-
A.
servedCity
Indicates that a service, route, or facility operates in, reaches, or is available to a particular city.
-
B.
hasCityRole
chosen
Indicates that an entity holds or is assigned a specific role, function, or status within a particular city.
-
C.
cityServedType
Indicates the type or category of city that is served by a given entity (such as a facility, service, or infrastructure).
-
D.
alternativeCityServed
Indicates that one city functions as an alternative service location for another city, typically in contexts like transportation or logistics.
-
E.
cityServedRegion
Indicates that a city provides services to, or functions as an administrative or economic center for, a specified region.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4bc90988190ac360aaf645efc1d |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6aadaf47881909e93efb535c6c1e3 |
completed | April 20, 2026, 10:38 p.m. |
| PD | Predicate disambiguation | batch_69e5a00c43308190b7ea58d559257e07 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:41 a.m.